Manufacturing Execution in the Age of IoT

The Internet of Things (IoT) is “connecting the physical world to the digital world,” says Alex Aminian, president & CEO of Decisyon, Inc. (decisyon.com). But what makes this acronymic technology different than the previous ones applied to factory automation is the internet and the vast amounts of real-time structured, semi-structured, and totally unstructured data ricocheting back and forth.

Getting a handle on this data-rich environment is key to the purpose of the Decisyon Digital Factory (DDF), a manufacturing execution system (MES) that unifies data, collaboration, decision-making, and execution; streamlines information flows; integrates plant operations; supports plant scheduling; and releases factory operators from being dependent on complex, monolithic, and expensive-to-modify software.

DDF, says Aminian, “delivers on the promise of IoT: unifying multiple data sources from applications, sensors, and analytics data; accelerating speed-to-action; facilitating human interaction; simplifying application evolution; completing the last mile of development; and scaling to meet performance demands. It leverages the three primary paradigms of IoT communication: machine-to-machine, machine-to-people, and people-to-people.”

Visual MES, Digital Cockpit and Asset Optimization

DDF is browser-based and, says Aminian, “agnostic about infrastructure.” Data within this MES can be visualized, sliced-and-diced, drilled down and analyzed every which way. It comes “out of the box” with data collection, data analysis, visualization, business intelligence, and more. Its three modules—Visual MES, Digital Cockpit, and Asset Optimization—incorporate data and service orchestration services, visualization and analytics, business rules management, in-context collaboration, and bidirectional connections to existing business enterprise systems (such as supervisory control and data acquisition, materials handling, warehouse management, and enterprise resource planning).

Visual MES provides real-time operations management. It’s the single point of access to all relevant data, including production data, line status, production order phases and quantities, ongoing information, and OEE. Users can track purchase order assignments, detect root causes for downtime and speed losses, and communicate with other users. The module also helps users mitigate operational problems before they affect productivity.

The Digital Cockpit is a visual dashboard showing everything from manufacturing processes to the connectivity between people across the user company. Users can drill down as much into the displayed data as desired; data views and analytics can be modified as required. The Digital Cockpit lets users attach tasks, ask questions, initiate communications, and dump data into reports—simply through right clicks. All data associated with manufacturing execution, monitoring, and operator actions are captured within a knowledge base for future problem resolution.

The Asset Optimization module integrates the real-time data from multiple systems and sources associated with asset management. The module lets operators from a central location monitor plants, production lines, and machines; schedule maintenance teams; and receive failure predictions. A key benefit of this module is its ability to view digital twins from sources such as historian systems, existing enterprise asset management, asset detail repositories, widgets, and manuals.

Integrated throughout DDF are business intelligence (BI), workflow and decision tree technology, and rules engines that automate tasks, including machine execution and alerting, based on past knowledge and expertise. All real-time data can be viewed collaboratively for collective decision-making. Third-party BI tools and analytics can be used for more sophisticated, predictive analytics and models.

Tweaking is allowed
 

Then there’s the Decisyon App Composer (DAC) that lets engineers, business analysts, developers, and non-developers drag-and-drop all the available services to rapidly build, modify, and customize data analytics, visual displays, workflows, and business/manufacturing rules—without IT’s help. “DAC was designed to help non-developers go from idea to revenue, idea to outcome, as fast as possible,” comments Aminian.

DAC “allows normal ‘citizen developers’ to easily build solutions that solve business problems, rather than worry about all the technical mumbo jumbo.”

DAC has three components: A Java-based standalone application for developing, configuring, and administering IoT business applications; a runtime for visualizing and interacting with presentation objects, as well as navigating and customizing app pages, BI, reports, and overall applications; and a database for storing design information and system configurations. DAC’s microservices architecture is written in Java, HTML 5, CSS 3. All data elements in DDF can be modified or acted upon, and it can run on any SQL database. Incidentally, while users can write their own rules using drag-and-drop, DDF has the capability for “super complex rules” to be coded conventionally.

Altogether, says Aminian, DAC “allows normal ‘citizen developers’ to easily build solutions that solve business problems, rather than worry about all the technical mumbo jumbo.”

At an affordable price

DDF comes either as a cloud-based subscription (think: rental) that’s priced based on plant size, number of users, operations complexity, and such, or installed on premises (think: in-house or cloud-based datacenter) that’s charged for a perpetual license (one-time cost plus annual maintenance fee). In the tiered subscription model (specifically, Platform as a Service, PaaS), a rough average cost for ten users is $200/month; for more than 100 users, about $50/month. For the perpetual license, the one-time cost ranges from $200,000 to $400,000; the annual maintenance fee, typically 20%. Customizations requiring Decisyon professional services cost extra.

For this, DDF provides all the conventional benefits of MES, including increased productivity through real-time information exchange, reduced data errors, and automated data collection; reduced costs through automated data collection and more timely intervention toward reducing costly production delays; improved operational communications through in-context, real-time, electronic collaboration between those involved in production (i.e., everyone in the enterprise); and the ability to optimize production.

DDF provides yet another benefit: Speedy implementation. At one manufacturing plant, the proof-of-concept regarding DDF, based on the manufacturer’s own operational data, took two weeks; installation a few hours; and testing took one month. Add training and customization. Start to finish: The manufacturer was running operations with DDF in three months.